def backward(ctx, *grad_output):
"""
In the backward pass we receive a Tensor containing the gradient of the loss
with respect to the output, and we need to compute the gradient of the loss
with respect to the input.
"""
*param_list, scale = ctx.saved_tensors
dscale_dL = sum(
grad.sum() for grad in torch._foreach_mul(param_list, grad_output))
dparam_dL = torch._foreach_mul(grad_output, float(scale))
return (dscale_dL, ) + dparam_dL
def _multi_tensor_rprop(params: List[Tensor], grads: List[Tensor],
prevs: List[Tensor], step_sizes: List[Tensor], *,
step_size_min: float, step_size_max: float,
etaminus: float, etaplus: float):
if len(params) == 0:
return
signs = torch._foreach_mul(grads, prevs)
signs = [s.sign() for s in signs]
for sign in signs:
sign[sign.gt(0)] = etaplus
sign[sign.lt(0)] = etaminus
sign[sign.eq(0)] = 1
# update stepsizes with step size updates
torch._foreach_mul_(step_sizes, signs)
for step_size in step_sizes:
step_size.clamp_(step_size_min, step_size_max)
# for dir<0, dfdx=0
# for dir>=0 dfdx=dfdx
for i in range(len(grads)):
grads[i] = grads[i].clone(memory_format=torch.preserve_format)
grads[i][signs[i].eq(etaminus)] = 0
# update parameters
grad_signs = [grad.sign() for grad in grads]
torch._foreach_addcmul_(params, grad_signs, step_sizes, value=-1)
for i in range(len(prevs)):
prevs[i].copy_(grads[i])
def adagrad(params: List[Tensor], grads: List[Tensor],
state_sums: List[Tensor], state_steps: List[int],
has_sparse_grad: bool, *, lr: float, weight_decay: float,
lr_decay: float, eps: float):
r"""Functional API that performs Adagrad algorithm computation.
See :class:`~torch.optim.Adagrad` for details.
"""
if weight_decay != 0:
if has_sparse_grad:
raise RuntimeError(
"weight_decay option is not compatible with sparse gradients")
torch._foreach_add_(grads, params, alpha=weight_decay)
minus_clr = [-lr / (1 + (step - 1) * lr_decay) for step in state_steps]
if has_sparse_grad:
# sparse is not supported by multi_tensor. Fall back to optim.adagrad
# implementation for sparse gradients
for i, (param, grad, state_sum,
step) in enumerate(zip(params, grads, state_sums,
state_steps)):
grad = grad.coalesce(
) # the update is non-linear so indices must be unique
grad_indices = grad._indices()
grad_values = grad._values()
size = grad.size()
state_sum.add_(_make_sparse(grad, grad_indices,
grad_values.pow(2)))
std_sparse = state_sum.sparse_mask(grad)
std_sparse_values = std_sparse._values().sqrt_().add_(eps)
param.add_(
_make_sparse(grad, grad_indices,
grad_values / std_sparse_values),
alpha=minus_clr[i],
)
else:
grads = [
torch.view_as_real(x) if torch.is_complex(x) else x for x in grads
]
state_sums = [
torch.view_as_real(x) if torch.is_complex(x) else x
for x in state_sums
]
torch._foreach_addcmul_(state_sums, grads, grads, value=1)
std = torch._foreach_add(torch._foreach_sqrt(state_sums), eps)
toAdd = torch._foreach_div(torch._foreach_mul(grads, minus_clr), std)
toAdd = [
torch.view_as_complex(x) if torch.is_complex(params[i]) else x
for i, x in enumerate(toAdd)
]
torch._foreach_add_(params, toAdd)
state_sums = [
torch.view_as_complex(x) if torch.is_complex(params[i]) else x
for i, x in enumerate(state_sums)
]
def forward(ctx, scale, *param_list):
"""
In the forward pass we receive a Tensor containing the input and return
a Tensor containing the output. ctx is a context object that can be used
to stash information for backward computation. You can cache arbitrary
objects for use in the backward pass using the ctx.save_for_backward method.
"""
ctx.save_for_backward(*param_list, scale.detach())
return torch._foreach_mul(param_list, float(scale))
def _multi_tensor_rprop(params: List[Tensor],
grads: List[Tensor],
prevs: List[Tensor],
step_sizes: List[Tensor],
*,
step_size_min: float,
step_size_max: float,
etaminus: float,
etaplus: float,
maximize: bool):
if len(params) == 0:
return
# Handle complex params
def _view_complex_as_real(tensor_list):
return [torch.view_as_real(t) if torch.is_complex(t) else t for t in tensor_list]
grads = _view_complex_as_real(grads)
prevs = _view_complex_as_real(prevs)
params = _view_complex_as_real(params)
step_sizes = _view_complex_as_real(step_sizes)
if maximize:
grads = torch._foreach_neg(grads)
signs = torch._foreach_mul(grads, prevs)
signs = [s.sign() for s in signs]
for sign in signs:
sign[sign.gt(0)] = etaplus
sign[sign.lt(0)] = etaminus
sign[sign.eq(0)] = 1
# update stepsizes with step size updates
torch._foreach_mul_(step_sizes, signs)
for step_size in step_sizes:
step_size.clamp_(step_size_min, step_size_max)
# for dir<0, dfdx=0
# for dir>=0 dfdx=dfdx
grads = list(grads)
for i in range(len(grads)):
grads[i] = grads[i].clone(memory_format=torch.preserve_format)
grads[i][signs[i].eq(etaminus)] = 0
# update parameters
grad_signs = [grad.sign() for grad in grads]
torch._foreach_addcmul_(params, grad_signs, step_sizes, value=-1)
for i in range(len(prevs)):
prevs[i].copy_(grads[i])
def _multi_tensor_adagrad(params: List[Tensor], grads: List[Tensor],
state_sums: List[Tensor], state_steps: List[Tensor],
*, lr: float, weight_decay: float, lr_decay: float,
eps: float, has_sparse_grad: bool):
# Foreach functions will throw errors if given empty lists
if len(params) == 0:
return
if has_sparse_grad is None:
has_sparse_grad = any([grad.is_sparse for grad in grads])
if has_sparse_grad:
return _single_tensor_adagrad(params,
grads,
state_sums,
state_steps,
lr=lr,
weight_decay=weight_decay,
lr_decay=lr_decay,
eps=eps,
has_sparse_grad=has_sparse_grad)
# Update steps
torch._foreach_add_(state_steps, 1)
if weight_decay != 0:
torch._foreach_add_(grads, params, alpha=weight_decay)
minus_clr = [-lr / (1 + (step - 1) * lr_decay) for step in state_steps]
grads = [
torch.view_as_real(x) if torch.is_complex(x) else x for x in grads
]
state_sums = [
torch.view_as_real(x) if torch.is_complex(x) else x for x in state_sums
]
torch._foreach_addcmul_(state_sums, grads, grads, value=1)
std = torch._foreach_add(torch._foreach_sqrt(state_sums), eps)
toAdd = torch._foreach_div(torch._foreach_mul(grads, minus_clr), std)
toAdd = [
torch.view_as_complex(x) if torch.is_complex(params[i]) else x
for i, x in enumerate(toAdd)
]
torch._foreach_add_(params, toAdd)
state_sums = [
torch.view_as_complex(x) if torch.is_complex(params[i]) else x
for i, x in enumerate(state_sums)
]
def multi_tensor_scale(
self,
src: Sequence[torch.Tensor],
dst: Sequence[torch.Tensor],
scale: float,
) -> None:
with torch.no_grad(): # type: ignore[no-untyped-call]
# _foreach_zero for long type is not supported in CUDA
if self._enable_foreach and src[0].is_floating_point():
# scale
val = torch._foreach_mul(tuple(src), scale)
# copy tensor
torch._foreach_zero_(tuple(dst))
torch._foreach_add_(tuple(dst), val)
else:
for s, d in zip(src, dst):
d.copy_(s * scale)
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
grads = []
states = []
params_with_grad = []
step_sizes = []
for group in self.param_groups:
for p in group['params']:
etaminus, etaplus = group['etas']
step_size_min, step_size_max = group['step_sizes']
if p.grad is not None:
if p.grad.is_sparse:
raise RuntimeError(
'RMSprop does not support sparse gradients')
grads.append(p.grad)
params_with_grad.append(p)
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
state['prev'] = torch.zeros_like(
p, memory_format=torch.preserve_format)
state['step_size'] = p.grad.new().resize_as_(
p.grad).fill_(group['lr'])
state['step'] += 1
states.append(state)
step_sizes.append(state['step_size'])
signs = torch._foreach_mul(grads, [s['prev'] for s in states])
signs = [s.sign() for s in signs]
for sign in signs:
sign[sign.gt(0)] = etaplus
sign[sign.lt(0)] = etaminus
sign[sign.eq(0)] = 1
# update stepsizes with step size updates
torch._foreach_mul_(step_sizes, signs)
for step_size in step_sizes:
step_size.clamp_(step_size_min, step_size_max)
# for dir<0, dfdx=0
# for dir>=0 dfdx=dfdx
for i in range(len(grads)):
grads[i] = grads[i].clone(memory_format=torch.preserve_format)
grads[i][signs[i].eq(etaminus)] = 0
# update parameters
grad_signs = [grad.sign() for grad in grads]
torch._foreach_addcmul_(params_with_grad,
grad_signs,
step_sizes,
value=-1)
for i in range(len(states)):
states[i]['prev'].copy_(grads[i])
return loss
def _multi_tensor_adamw(params: List[Tensor], grads: List[Tensor],
exp_avgs: List[Tensor], exp_avg_sqs: List[Tensor],
max_exp_avg_sqs: List[Tensor],
state_steps: List[Tensor], *, amsgrad: bool,
beta1: float, beta2: float, lr: float,
weight_decay: float, eps: float, maximize: bool,
capturable: bool):
if len(params) == 0:
return
if capturable:
assert all(p.is_cuda and step.is_cuda for p, step in zip(params, state_steps)), \
"If capturable=True, params and state_steps must be CUDA tensors."
if maximize:
grads = torch._foreach_neg(tuple(grads)) # type: ignore[assignment]
grads = [
torch.view_as_real(x) if torch.is_complex(x) else x for x in grads
]
exp_avgs = [
torch.view_as_real(x) if torch.is_complex(x) else x for x in exp_avgs
]
exp_avg_sqs = [
torch.view_as_real(x) if torch.is_complex(x) else x
for x in exp_avg_sqs
]
params = [
torch.view_as_real(x) if torch.is_complex(x) else x for x in params
]
# update steps
torch._foreach_add_(state_steps, 1)
# Perform stepweight decay
torch._foreach_mul_(params, 1 - lr * weight_decay)
# Decay the first and second moment running average coefficient
torch._foreach_mul_(exp_avgs, beta1)
torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1)
torch._foreach_mul_(exp_avg_sqs, beta2)
torch._foreach_addcmul_(exp_avg_sqs, grads, grads, 1 - beta2)
if capturable:
# TODO: use foreach_pow if/when foreach_pow is added
bias_correction1 = [torch.pow(beta1, step) for step in state_steps]
bias_correction2 = [torch.pow(beta2, step) for step in state_steps]
# foreach_sub doesn't allow a scalar as the first arg
torch._foreach_sub_(bias_correction1, 1)
torch._foreach_sub_(bias_correction2, 1)
torch._foreach_neg_(bias_correction1)
torch._foreach_neg_(bias_correction2)
# foreach_div doesn't allow a scalar as the first arg
step_size = torch._foreach_div(bias_correction1, lr)
torch._foreach_reciprocal_(step_size)
torch._foreach_neg_(step_size)
bias_correction2_sqrt = torch._foreach_sqrt(bias_correction2)
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
torch._foreach_maximum_(max_exp_avg_sqs, exp_avg_sqs)
# Use the max. for normalizing running avg. of gradient
max_exp_avg_sq_sqrt = torch._foreach_sqrt(max_exp_avg_sqs)
# Folds in (admittedly ugly) 1-elem step_size math here to avoid extra param-set-sized read+write
# (can't fold it into addcdiv_ below because addcdiv_ requires value is a Number, not a Tensor)
torch._foreach_div_(
max_exp_avg_sq_sqrt,
torch._foreach_mul(bias_correction2_sqrt, step_size))
eps_over_step_size = torch._foreach_div(step_size, eps)
torch._foreach_reciprocal_(eps_over_step_size)
denom = torch._foreach_add(max_exp_avg_sq_sqrt, eps_over_step_size)
else:
exp_avg_sq_sqrt = torch._foreach_sqrt(exp_avg_sqs)
torch._foreach_div_(
exp_avg_sq_sqrt,
torch._foreach_mul(bias_correction2_sqrt, step_size))
eps_over_step_size = torch._foreach_div(step_size, eps)
torch._foreach_reciprocal_(eps_over_step_size)
denom = torch._foreach_add(exp_avg_sq_sqrt, eps_over_step_size)
torch._foreach_addcdiv_(params, exp_avgs, denom)
else:
bias_correction1 = [1 - beta1**step.item() for step in state_steps]
bias_correction2 = [1 - beta2**step.item() for step in state_steps]
step_size = [(lr / bc) * -1 for bc in bias_correction1]
bias_correction2_sqrt = [math.sqrt(bc) for bc in bias_correction2]
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
torch._foreach_maximum_(max_exp_avg_sqs, exp_avg_sqs)
# Use the max. for normalizing running avg. of gradient
max_exp_avg_sq_sqrt = torch._foreach_sqrt(max_exp_avg_sqs)
torch._foreach_div_(max_exp_avg_sq_sqrt, bias_correction2_sqrt)
denom = torch._foreach_add(max_exp_avg_sq_sqrt, eps)
else:
exp_avg_sq_sqrt = torch._foreach_sqrt(exp_avg_sqs)
torch._foreach_div_(exp_avg_sq_sqrt, bias_correction2_sqrt)
denom = torch._foreach_add(exp_avg_sq_sqrt, eps)
torch._foreach_addcdiv_(params, exp_avgs, denom, step_size)
